It is a crisp December morning, with frost on every leaf and a warm winter sun amidst blue skies. There may even be a robin or two amongst the trees. Christmas is in the air and there is a hive of activity here to deliver something special—a sofa, fridge, or TV—in time for the holiday for thousands of John Lewis customers. As the convoy of shiny black liveried trucks, each laden with its stock of large items, makes its way out of the site and on towards customers across the country, the scale of this operation becomes apparent.

John Lewis Partnership (JLP) is an iconic British retailer and its renowned customer loyalty has a long heritage. As the retail market and society have changed, particularly post-Covid, the focus on the customer promise has never been higher. For large items like refrigerators, furniture, or anything requiring a two-person delivery, this means delivering what the customer ordered, when they expect it, leaving them delighted with the experience. Every time.

Optimizing and improving the supply chain to support these objectives is a priority for any retailer. Traditionally, this has relied on the deep expertise within supply chain teams to deliver the scale, service quality and efficiencies required by the business. Forward looking retailers are now looking to augment this by starting to explore the promise of a digital twin of their supply chain. The aspiration is to discover novel possibilities and explore opportunities for solving existing and future business challenges.

What is a digital twin?

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

Find out more

The John Lewis supply chain team engaged with IBM Client Engineering to explore the initial steps toward a digital twin. Alongside this strategic aim were more tactical, but still important, supply chain planning objectives that were good candidates for a more data-driven approach. Digital twins can serve as a sandbox for testing new supply chain strategies and technologies without affecting the physical supply chain, enabling continuous improvement and learning.

Why IBM Client Engineering?

Using the Value Engineering Method we take a human approach to solving complex business problems with transformative technology. Using a proven co-creation methodology we align experts from both parties to identify a business challenge that could be the focus of a PoX (Proof of Experience).

What data challenges and opportunities do you have?

According to Anthony Olver, Supply Chain Planning Manager for the John Lewis Partnership, “We have the benefit at JLP of many years of historic data but in many different formats. As a result, datasets were in different locations, with differing levels of access and under the purview of different teams within the partnership. It was therefore not always efficient to bring it together in a useful way.”

“We wanted greater visibility of the present and the ability to easily look back to gauge performance and KPIs. We also had a desire to do more modelling of the art of the possible and future scenarios to give the business the right insights to make the right future investments.”

Presenting all this in a user-friendly interface that makes it easy to navigate the past, present, and future in a natural way was important for adoption. Achieving this would allow business users to make the most of the data in a way that is relevant to their role.

Our Partnership Data Plan, which centers around Snowflake, is our strategic approach to address this. Our project highlighted the potential of more real-time data and the benefits of having data in a single logical location. It has given a glimpse of the future.

What was JL’s experience of the Client Engineering process?

“Working with the Client Engineering team provided us with an exciting opportunity to co-create and explore innovative ways to increase efficiency within our supply chain,” said Stewart Dean, Supply Chain Planning & Development Lead, John Lewis Partnership.

Two streams ran at speed in parallel—stakeholder interviews leading to a UI wire-frame design, and development of the UI in React alongside design and population of the data model. These streams were synced through daily stand-ups which provided an opportunity to rapidly pivot and take on new requests, such as including carbon usage and customer satisfaction data.

The momentum this process generated energized the IBM and John Lewis teams. Weekly playbacks were a celebration of the progress and an opportunity for senior stakeholders to provide their input.

What possibilities did this data visualization project highlight?

“We saw the art of the possible and the scale of the opportunity,” says Anthony Olver. “The magic was how IBM created the data model and brought it all together in a UI design such that the data and insights were easily navigable. This has fantastic potential and puts us on the path to a Digital Twin of our supply chain.”

Stewart Dean went on to say, “The IBM team were able to absorb our complex data sets and transform them into a dynamic and interactive MVP performance dashboard, providing our teams with new capabilities, allowing them to easily assimilate data and gain key performance insights in a highly visual way.”

The screenshots below show the complexity within datasets such as for supply chain, customer orders and revenue being represented in relation to each other in a dynamic interactive dashboard.

UI design for supply chain scenario modelling (using dummy data)
UI design for supply chain network performance heatmap (using dummy data)

UI design for supply chain network performance mapped against delivery routes (using dummy data)

Where do you see this going next?

According to Anthony Olver, “This has massive potential in terms of giving us the ability to visualize the supply chain with different perspectives across sites, group, regions, overlaid with specific metrics and timelines. We would have a view of overall performance right down to individual items. This would give us the ability to make data-enriched strategic and tactical decisions.”

Client Engineering is IBM’s investment in its customers to help solve tough business challenges through co-creation. We are designers, data scientists, technology engineers and business analysts who love working with customers and bringing IBM technology to life.

Do you have a business challenge you would like to approach in a different way? Contact your local IBM representative or click the button to learn more.

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